The Smallholder Data Gap

Smallholder farms — holdings of less than 2 hectares — produce one-third of global food consumed, employ 2 billion people, and make up 84% of the world’s farms. Yet our collective knowledge of their food production remains limited: questions like what crop types smallholders are growing and where they grow them remain unanswered, making it hard to track yield progress, study farming practices, and design agricultural policies. The data gap exists because most smallholders are located in countries where the ability to conduct surveys — the traditional way of obtaining farm-level information — is still nascent or under development.

In recent years, satellite imagery has become available at higher spatial and temporal resolutions than ever before, offering another option to observe smallholder systems at low cost and global scale. At the same time, advances in machine learning — deep learning in particular — have enabled the automatic extraction of insights from unstructured data like images, text, and audio. By applying machine learning methods to satellite imagery, researchers have created maps of forest cover and cropland all around the world. But training machine learning methods requires ground truth labels; in agriculture, where labels are usually collected through surveys, this brings us back to looking for sources of ground truth in smallholder systems.­

The Potential of Smartphone Crowdsourcing

While surveys and other infrastructure are often lacking in developing countries, one technology has become widespread: the mobile phone. Even in Sub-Saharan Africa, there are now 82 mobile cellular subscriptions for every 100 people, compared to 48 percent of the population with access to electricity (World Bank Open Data 2018). Such widespread mobile phone use opens up the possibility of collecting data directly from individuals, and previous works explored this option for gathering household data and mapping poverty (Dillion 2012, Blumenstock et al. 2015). In the case of classifying crop types in satellite imagery, the ideal crowdsourced dataset would be people telling us what type of crop is growing at specific geolocated coordinates.
“Hey Siri, what’s wrong with my crop?”

Where can we find such a dataset? Enter Plantix, a free mobile app that helps farmers diagnose what’s wrong with their crops. Farmers submit a photo of their plant through the app and receive a diagnosis of disease or nutrient deficiency along with a treatment plan. To produce this diagnosis, Plantix feeds the farmer’s photo through a deep neural network that classifies the crop type and illness present in the photo. The location of the farmer’s mobile phone is logged when the photo is taken, resulting in a dataset of crop types at geolocated coordinates — exactly what we’re looking for!
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Our study is the first to explore using crowdsourced data to augment or replace ground surveys for land use mapping at a large scale. With quality control steps like filtering on location accuracy and resampling geographically, datasets like Plantix can be used to map land use, and a moderate amount of noise is acceptable in the training set. Work remains to remove lingering noise in the validation set, mask out clouds in satellite imagery, and ensure that crowdsourced samples are representative of the population. Still, as mobile phones and internet access become even more widespread around the world, crowdsourcing is poised to become an increasingly useful alternative to traditional fieldwork.